Satya Nadella: evaluation

Transcript context

…Right. In fact, in the early days of hyperscale, most people thought “there are all these hosters, and those are not great businesses. Will there be anything? Is there a business even in hyperscale?” And it turns out there is a real business, just because of the know-how of running, in the case of Azure, the world's computing of 60-plus regions with all the compute. It's just a tough thing to duplicate. So I was more making the point, is it one winner? Is it a winner-take-all or not? Because that you've got to get right. I like to enter categories which are big TAMs, where you don't have to have the risk of it all being winner-take-all. The best news to be in is a big market that can accommodate a couple of winners, and you're one of them. That's what I meant by the hyperscale layer. In the model layer, one is models need ultimately to run on some hyperscale compute. So that nexus, I feel, is going to be there forever. It's not just the model; the model needs state, that means it needs storage, and it needs regular compute for running these agents and the agent environments. And so that's how I think about why the limit of one person running away with one model and building it all may not happen. On the hyperscaler side, and by the way, it's also interesting the advantage you as a hyperscaler would have in the sense that, especially with inference time scaling and if that's involved in training future models, you can amortize your data centers and GPUs, not only for the training, but then use them again for inference. I'm curious what kind of hyperscaler you consider Microsoft and Azure to be. Is it on the pre-training side? Is it on providing the O3-type inference? Or are you just, we’re going to host and deploy any single model that's out there in the market, and we are sort of agnostic about that?…

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